Bayesian foreground segmentation and tracking using pixel-wise background model and region-based foreground model
Document typeConference report
PublisherIEEE Press. Institute of Electrical and Electronics Engineers
Rights accessOpen Access
In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/ background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground is modeled using a Gaussian Mixture Model with feature vectors consisting of the spatial (x, y) and colour (r, g, b) components. The spatial components of this model are updated using the Expectation Maximization algorithm after the classification of each frame. The background model is formulated in the 5 dimensional feature space in order to be able to apply a Maximum A Posteriori framework for the classification. The classification is done using a graph cut algorithm that allows taking into account neighborhood information. The results presented in the paper show the improvement of the system in situations where the foreground objects have similar colors to those of the background.
CitationGallego, J.; Pardas, M.; Haro, G. Bayesian foreground segmentation and tracking using pixel-wise background model and region-based foreground model. A: IEEE International Conference on Image Processing. "16th IEEE International Conference on Image Processing". Cairo: IEEE Press. Institute of Electrical and Electronics Engineers, 2009, p. 3205-3208.
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